3D blending and illumination of seismic volumes for automatic derivation of discontinuities

ABSTRACT

The present disclosure describes computer-implemented methods, computer-program products, and computer systems, for providing parameters for successful automated fault patch extraction. Attributes are selected for annotating images generated from, and for interpretation of, seismic amplitude volume. Images are generated from layers of a seismic cube, each generated using a different attribute of the plural attributes. The plural images are blended using customized palettes and initial parameters to create a blended image illuminating discontinuities in the layers. Optimal parameters are iteratively determined for automatic derivation of fault discontinuities on an interpreter-selected edge-enhanced sub-volume. The iterations are controlled and terminated based on interpreter inputs. The optimal extraction parameters are applied to an entire edge-enhanced volume. Important extracted fault discontinuities are isolated using commercial filtering tools. Extracted fault patches are refined based on received manual interpretation. Patch results are converted to traditional fault objects for further interpretation and refinement.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation Application of and claims the benefitof priority to U.S. patent application Ser. No. 15/013,717, filed onFeb. 2, 2016, the contents of which are hereby incorporated byreference.

BACKGROUND

For many decades, geophysicists have strived to extract and utilizeinformation from seismic data to generate oil and gas prospects. Forexample, the identification and interpretation of fault discontinuitiescan be critical to their success because faults are natural barriersthat can prevent (or facilitate) fluid flow and often provide theessential trap for hydrocarbon reservoirs. Delineating and interpretingsuch discontinuities rapidly under today's increasingly challengingenvironment can depend, for example, on effective use of the bestmethodologies and technologies, but this is not necessarily done inpractice. For example, while valuable information about seismicdiscontinuities may be encompassed in 3D seismic volumes, extracting andutilizing the information may not be achieved in practice.

SUMMARY

The present disclosure describes methods and systems, includingcomputer-implemented methods, computer-program products, and computersystems, for providing parameters for successful automated fault patchextraction. Plural attributes are selected for annotating imagesgenerated from, and for interpretation of, seismic amplitude volume.Plural images are generated from a set of layers of a seismic cube, eachimage generated using a different attribute of the plural attributes.The plural images are blended using customized palettes and initialparameters for each of the plural images. The blending creates a blendedimage that illuminates discontinuities in the set of layers. Optimalparameters are iteratively determined for automatic derivation of faultdiscontinuities on an interpreter-selected edge-enhanced sub-volume. Theiterations are controlled and terminated based on inputs from aninterpreter. The optimal extraction parameters are applied to an entireedge-enhanced volume. Important extracted fault discontinuities areisolated using commercial filtering tools. Extracted fault patches arerefined based on received manual interpretation from the interpreter.Patch results are converted to traditional fault objects for furtherinterpretation and refinement.

These methods, computer-program products, and computer systems describedherein can utilize commercially available seismic attributes andexploration software. The techniques described below provide amethodology for revealing potentially hidden discontinuities in seismicdata and improving automated fault extraction results. For example, morevisible and meaningful discontinuities and faults can be converted backto conventional fault objects for further interactive interpretation. Aworkflow methodology is described below that blends varying percentagesof multiple 3D seismic volume attributes to optimally illuminate andautomatically interpret fault discontinuities in seismic data. Theworkflow can significantly improve overall turnaround time and canenhance the process of delineating structural seal and leak potentialrequired during prospect generation.

In some implementations, an original seismic amplitude volume can beused as input to generate three additional attribute volumes, referredto in this text as pre-conditioned, edge-detected, and edge-enhancedattributes. These attribute volumes can be initially viewedindividually, and their characteristics can be enhanced by applyingspecific color palette customization to emphasize potential faultdiscontinuities. Each of the volumes can be optimized in this way beforeblending the volumes together by applying appropriate transparencies toilluminate the seismic discontinuities to their full extent. Thesuccessful implementation of this stage can be critical in determiningmeaningful results in the subsequent automated fault extraction andinterpretation.

After illumination of the discontinuities by volume blending, optimalextraction parameters can be determined iteratively over arepresentative sub-volume, before applying refined parameters to thefull data extent. Waiting to use the full data extent is necessarybecause the automated extraction process is typically CPU-intensive andmay take many hours to complete on a large, 3D seismic volume. Thespecific parameters applied can depend on the commercial software beingused as well as the characteristics and quality of the input seismicdata. Each geological scenario can typically require its own uniqueextraction parameters.

The extracted discontinuities can then be filtered using software tools,such as a stereonet, for directional fault attributes that aresymptomatic of prevailing geological stress fields. Also, histogramtools can be used to isolate discontinuities based on other propertiessuch as dip azimuth, surface area, confidence, extent, and otherproperties. The isolated extracted results can then be interpreted andcombined into meaningful geological discontinuities using tools (e.g.,merge, smooth, interpret, extend, and delete) before finally convertingthe isolated extracted results to normal fault interpretation objectsfor further refinement in the interpretation process. Using thisprocess, extracted results can be produced in a matter of hours,followed by further manual refinement by the interpreter, beforeconverting the results to traditional fault objects. This undertakingcan otherwise require many weeks to interpret using traditionalnon-automated fault interpretation methods.

By incorporating components of automation and critical human input, thetechniques described herein can deliver rapid, objective, and subjectiveinterpretation of fault discontinuities that can significantly improveprospect generation turnaround time. The innovation can be independentof specific software and can be applied using industry recognizedseismic exploration applications that use appropriate multi-volumeseismic attributes. The quality of the results can be heavily influencedby the quality and effectiveness of blending during the initial stages,and also by the subsequent selection of parameters used for automaticfault extraction. The extracted results can be refined into meaningfuldiscontinuities by the interpreter and then converted into regular faultdata for final interpretation.

The techniques described herein can combine efficiencies of process andalgorithmic objectivity in generating automated extracted results withthe essential human control, e.g., that is necessary to guide theprocess by blending and illumination of discontinuities in the initialstages and by manual refinement of the extracted results in the latterstages.

Other implementation aspects of the techniques described herein caninclude corresponding computer systems, apparatuses, and computerprograms recorded on one or more computer-readable media/storagedevices, each configured to perform the actions of the methods. A systemof one or more computers can be configured to perform particularoperations or actions by virtue of having software, firmware, hardware,or a combination of software, firmware, or hardware installed on thesystem that in operation causes or causes the system to perform theactions. One or more computer programs can be configured to performparticular operations or actions by virtue of including instructionsthat, when executed by data processing apparatus, cause the apparatus toperform the actions.

For example, one computer-implemented method can include: selectingplural attributes for annotating images generated from, and forinterpretation of, seismic amplitude volume; generating plural imagesfrom a set of layers of a seismic cube, each image generated using adifferent attribute of the plural attributes; blending the pluralimages, using customized palettes and initial parameters for each of theplural images, the blending creating a blended image that illuminatesdiscontinuities in the set of layers; iteratively determining optimalparameters for automatic derivation of fault discontinuities on aninterpreter-selected edge-enhanced sub-volume, wherein iterations arecontrolled and terminated based on inputs from an interpreter; applyingthe optimal extraction parameters to an entire edge-enhanced volume;isolating important extracted fault discontinuities using commercialfiltering tools; refining extracted fault patches based on receivedmanual interpretation from the interpreter; and converting patch resultsto traditional fault objects for further interpretation and refinement.

The foregoing and other implementations can each optionally include oneor more of the following features, alone or in combination:

A first aspect, combinable with the general implementation, wherein themethod further comprises receiving the seismic cube.

A second aspect, combinable with any of the previous aspects, whereinthe plural attributes include pre-conditioned seismic attributes,edge-detection attributes, and edge-enhanced attributes, and whereinblending the plural images includes blending images generated from thepre-conditioned seismic attributes, the edge-detection attributes, andthe edge-enhanced attributes.

A third aspect, combinable with any of the previous aspects, wherein theoptimal parameters for the pre-conditioned seismic attributes, theedge-detection attributes, and the edge-enhanced attributes includeopacities of 70%, 40%, and 30%, respectively.

A fourth aspect, combinable with any of the previous aspects, whereinthe initial parameters include a 10% extraction sampling threshold.

The subject matter described in this specification can be implemented inparticular implementations so as to realize one or more of the followingadvantages. First, identification of discontinuities during prospectgeneration can be critical to the viability of a potential prospect. Forexample, failure to identify structural seals or barriers that impacthydrocarbon migration can result in the prospect being misinterpreted oreven completely overlooked, costing potentially millions of dollars to acompany. Second, discontinuities that are clearly visible usingtechniques described herein may be almost entirely concealed using othertechniques. For example, critical seismic discontinuities may be hiddento the interpreter if inadequate methodologies or limited softwarecapabilities are applied during the interpretation process. Third, thetechniques combine automation and human input to maximize interpretationresults and also reduces turnaround time, saving potentially millions ofdollars. Fourth, the techniques described herein can utilize industryrecognized seismic attributes and can incorporate the attributes into acomprehensive methodology for automated interpretation of seismicdiscontinuities. Fifth, workflows described herein can combine advancedillumination methodologies with a robust procedure for maximizing thepotential of automatic fault extraction algorithms. When these componentparts are used in conjunction, then the overall benefits can far exceedthe sum of their individual parts. For example, the individual seismicattributes are of reduced value if they are not effectively blendedtogether and illuminated to maximize the information contained withinthem. Further, automated fault extraction algorithms are less effectiveif they are not guided by human input, which must first be determined byillumination of the information contained within the seismic data.Sixth, the combination of elements in the workflow described below canprovide a geophysical interpreter with a methodology that can ensurequick delivery of interpretation results leveraging the finest aspectsof automation and human input to deliver high-value results in theprospect generation process. Other advantages will be apparent to thoseof ordinary skill in the art.

The details of one or more implementations of the subject matter of thisspecification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a flow diagram of an example workflow for blending multiple 3Dseismic volume attributes and applying techniques to makediscontinuities stand out, according to an implementation.

FIG. 2 is a table listing example parameters used for automaticdiscontinuity extraction, according to an implementation.

FIG. 3 is a diagram showing an example stereonet plot showing filteredextracted discontinuities, according to an implementation.

FIG. 4 is a diagram illustrating an example histogram graphicallyrepresenting a filtering of extracted discontinuities by properties,according to an implementation.

FIG. 5 shows an image illustrating an original seismic volume, accordingto an implementation.

FIG. 6 shows an image illustrating pre-conditioned seismic volume,according to an implementation.

FIG. 7 shows an image illustrating edge-detected volume, according to animplementation.

FIG. 8 shows an image illustrating edge-enhanced volume using anenhanced color palette, according to an implementation.

FIG. 9 shows an image illustrating initial 3D blended volumes, accordingto an implementation.

FIG. 10 shows an image illustrating optimally 3D blended and illuminatedseismic cubes, according to an implementation.

FIG. 11 shows an image illustrating automatically extracteddiscontinuities, according to an implementation.

FIG. 12 shows an image illustrating automatically extracteddiscontinuities, according to an implementation.

FIG. 13 shows an illustrative workflow for refining extraction results,according to an implementation.

FIG. 14 illustrates a table of example color spectrums used forconstraints, according to an implementation.

FIG. 15 illustrates a method 1500 for blending multiple 3D seismicvolume attributes and applying techniques to make discontinuities standout, according to an implementation.

FIG. 16 is a block diagram illustrating an exemplary distributedcomputer system (EDCS) used for providing parameters for successfulautomated fault patch extraction, according to an implementation.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

This disclosure generally describes methods and systems, includingcomputer-implemented methods, computer-program products, and computersystems, for providing parameters for successful automated fault patchextraction.

The following subject matter is presented to enable any person skilledin the art to make and use the invention, and is provided in the contextof one or more particular implementations. Various modifications to thedisclosed implementations will be readily apparent to those skilled inthe art, and the general principles defined herein may be applied toother implementations and applications without departing from scope ofthe disclosure. Thus, the present disclosure is not intended to belimited to the described and/or illustrated implementations, but is tobe accorded the widest scope consistent with the principles and featuresdisclosed herein. Additionally, although images shown in the includedfigures are presented in grayscale, implementations involving colorpalettes can be used and can provide color-annotated results that aremore easily processed visually by a user.

In typical implementations, specific blending parameters and customizedcolor palettes of industry standard conditioned seismic, edge-detectionattributes, and edge-enhancement attribute cubes can be combined toeffectively illuminate discontinuities, which can then be used tofacilitate an iterative refinement of parameters for successfulautomated fault patch extraction. As a result, important seismicdiscontinuities that might otherwise remain hidden using traditionalmethodologies can be effectively illuminated, and turnaround time foriterative automated fault extraction and interpretation can bedecreased.

Discontinuities in seismic data are generally caused by changes ofphysical rock properties within a short distance. For example, rockproperties that effect seismic response include, but are not limited to,density, velocity, and porosity. The possible causes of sudden rockproperty changes can be diverse and may be due to earth movement (e.g.,faulting/fracturing), variations in depositional process (e.g.,lithology variation), an interruption of sedimentation and subsequenterosion (e.g., unconformity), or other causes. Movement of intrusiveentities, such as salt or igneous intrusions, can also induce stressbuildup and can cause faulting and fracturing. A sudden change of rockproperties can have major implications for the viability of a potentialprospect or field development, which is why the identification ofdiscontinuities is of paramount importance.

In some implementations, seismic data can include reflections collectedfrom acoustic waves traversed through the strata beneath the earth'ssurface. When rock properties change in a faulted, fractured orunconformable environment, for example, the change in acoustic impedancecan produce a reflection that is recorded at the receiver. This seismicrecord can be used to understand the subsurface geometry and detectsubtle differences in rock property and displacement. Rock movement overmillions of years can produce a complex and sometimes noisy seismicsignature that can mask important discontinuities, which if overlookedcan have major repercussions, for example, for prediction of reservoirseal and fluid flow.

This disclosure describes an example workflow to blend multiple 3Dseismic volumes and apply illumination techniques to makediscontinuities stand out and improve the iterative process of automaticderivation of discontinuities. For example, different seismic attributessuch as structural smooth, edge-detection, coherency, chaos, oredge-enhanced attributes, all contain varying degrees of informationrelated to reservoir discontinuities. The different seismic attributesmay, however, also contain undesirable characteristics that reduce theireffectiveness when used independently. In some implementations, byilluminating a percentage of each volume and applying specific colorpalettes to each attribute, it can be possible to blend multiple 3Dseismic attribute volumes together to emphasize discontinuities. Oncethe discontinuities are illuminated for example, iterative adjustment offault patch extraction parameters using commercial exploration tools canbe made much easier. By adjusting extraction sampling thresholds,connectivity constraints, and minimum fault patch size, for example, theimportant discontinuities can be automatically derived, isolated, andinterpreted. The more meaningful discontinuities and extracted faultpatches can then be converted and incorporated into traditionalinterpretation processes. The automatically extracted components canprovide, for example, an effective starting point in prospect generationthat is both quick and accurate, which can then be refined bytraditional interpretation.

The majority of seismic data acquired for exploration and analysis ofreservoirs can be based, for example, on seismic reflection technology.For example, 3D seismic data can provide a 3-dimensional representationof reservoir geometry beneath the earth's surface. Geological structuresand rock discontinuities can be imaged on seismograms, e.g., to enableidentification of structural seals for hydrocarbon traps or, conversely,to enable identification of pathways for hydrocarbon escape. It cantherefore be critical to be able to identify and delineate rockdiscontinuities early in the prospect generation process.

A seismic cube is a 3D representation of reflected acoustic waves fromthe earth's subsurface. The seismic cube can be recorded either onshoreor offshore by service companies using seismic acquisition technology.An example acquisition company is WESTERN GECO. The seismic cube can bestored in a Society of Exploration Geophysicists (SEGY) format and canbe represented and/or stored in a standard industry format that can beimported into commercially available exploration software, such asPETREL. Seismic acquisition is a major component of oil exploration andis a cost-effective way of imaging large areas without drillingmulti-million dollar exploration wells. In typical implementations, aseismic cube can range in size from a few megabytes (MB) to multiplegigabytes (GB) of data.

Physical rock properties change over geological time. Although minorchanges can be readily observed in rock outcrops, the changes may notnecessarily be apparent in the seismic record. For example, seismicreflections, by nature, are a product of many complex interactions, andnoise can often mask important reflections from what can be criticaldiscontinuities of a potential reservoir. Finding ways to reduce theundesirable effects of noise, while enhancing meaningful reflections in3D seismic data, for example, can be a continual challenge in theexploration process.

FIG. 1 is a flow diagram of an example workflow 100 for blendingmultiple 3D seismic volume attributes and applying techniques to makediscontinuities stand out, according to an implementation. For example,the workflow 100 can be used with techniques described herein to blendmultiple 3D seismic volume attributes.

At 102, the workflow 100 begin with a received seismic cube. Forexample, the received seismic cube can include seismic data in threedimensions, providing a 3D representation of reflected acoustic wavesfrom the earth's subsurface. The X and Y dimensions of the seismic cubecan be associated with a two-dimensional representation of the earth'ssurface. The Z dimension of the seismic cube can correspond to time,e.g., with each Z-dimension slice in the seismic cube corresponding to adifferent time (or the Z-dimension can correspond to depth). Asdescribed above, the seismic cube can be recorded either onshore oroffshore by service companies using seismic acquisition technology.Information from the seismic cube can be used, for example, to generateimages shown in FIGS. 5-15. From 102, workflow 100 proceeds to 104.

At 104, different seismic attributes can be extracted from the seismiccube. In typical implementations, the seismic attributes can include,for example, pre-conditioned seismic attributes, various edge-detectionattributes, and edge-enhanced attributes, among other attributes. Eachattribute is optimized independently by refinement of associatedparameters. The attributes can contain and/or be associated withinformation that is related, for example, to geological discontinuities.For example, images associated with pre-conditioned seismic attributes,various edge-detection attributes, and edge-enhanced attributes aredescribed below with reference to FIGS. 6, 7, and 8, respectively. From104, workflow 100 proceeds to 106.

At 106, customized color palettes are applied. For example, colorspectrums 1402, described below with reference to FIG. 14, can beapplied. From 106, workflow 100 proceeds to 108. For example, an imageillustrating edge-enhanced volume using an enhanced color palette isdescribed below with reference to FIG. 8.

At 108, blending can be used to illuminate discontinuities. For example,illuminating a percentage of each volume of the attributes, manipulatingcolor palettes (e.g., to enhance contrast), and blending the attributestogether can reveal previously hidden discontinuities. For example, animage illustrating initial 3D blended volumes is described below withreference to FIG. 9, and image illustrating optimally 3D blended andilluminated seismic cubes is described below with reference to FIG. 10.From 108, workflow 100 proceeds to 110. Blending the plural images caninclude using specific customized color palettes (e.g., color spectrums1402 described below with reference to FIG. 14).

At 110, the illuminated volume can then be used as a base to iterativelyoptimize sampling thresholds to capture minimum signal levels. Forexample, an image illustrating automatically extracted discontinuitiesis described below with reference to FIG. 11. The image is generatedusing an extraction sampling threshold of Top 10%, e.g., a minimumsignal level from which to create extraction points. An image generatedusing the extraction sampling threshold of 50% is described below withreference to FIG. 12. From 110, workflow 100 proceeds to 112.

At 112, the results of 110 are visualized. The minimum signal levels areused to extract fault patches, voxel connectivity constraints, andminimal fault patch sizes for automated fault extraction. From 112,workflow 100 proceeds to 114. At 114, final fault patch extractionparameters are applied to the entire edge-enhanced volume. From 114,workflow 100 proceeds to 116. At 116, important extracted faultdiscontinuities are isolated, e.g., using commercial filtering tools.From 116, workflow 100 proceeds to 118. At 118, the automaticallyextracted discontinuities can then be refined manually, e.g., usingcommercial exploration software. From 118, workflow 100 proceeds to 120.

At 120, the patch results are converted to regular fault interpretationobjects for further detailed interpretation and analysis. After 120,workflow 100 stops.

The value of the workflow 100 can include, for example, the ability ofthe workflow to effectively identify discontinuities that may otherwisebe concealed in the seismic data. Further, the workflow 100 can alsoprovide a significant reduction in turnaround time for interpretinglarge 3D survey areas. The automated component of derivingdiscontinuities can leverage algorithmic objectivity, which isexclusively data dependent with human control and guidance to improvefinal interpretation accuracy.

FIG. 2 is a table 200 listing example parameters used for automaticdiscontinuity extraction, according to an implementation. For example,the parameters can be provided by (and/or included in) commerciallyavailable exploration software. As such, a person of skill in the artcan use such commercially available exploration software in support ofthe techniques described herein.

FIG. 3 is a diagram showing an example stereonet 300 plot showingfiltered extracted discontinuities, according to an implementation. Forexample, the discontinuities can be filtered according to tectonicstress fields and dip orientation, e.g., using commercially availableexploration software.

FIG. 4 is a diagram illustrating an example histogram 400 graphicallyrepresenting a filtering of extracted discontinuities by properties,according to an implementation. For example, histogram plotting andassociated properties can be provided by commercially availableexploration software. The properties can include, for example, dipazimuth, dip, surface area, azimuth in seismic, dip in seismic,confidence, and extent.

In an example that follows, described with reference to FIGS. 5-13, dataand results are based on a representative seismic time section in theRed Sea at a depth of −1128 ms. The examples illustrate how seismicdiscontinuities can be concealed unless effective methodologies are usedto illuminate them.

FIG. 5 shows an image 500 illustrating an original seismic volume,according to an implementation. For example, the image 500 shows seismicdata of a time slice at −1128 ms. As shown, the fault discontinuitiesare indistinct even though the seismic quality is good. The volumeprovides clear information relating to seismic horizons. A palette 502indicates a spectrum of colors representing changes in the image.

FIG. 6 shows an image 600 illustrating pre-conditioned seismic volume,according to an implementation. For example, the original seismic volumecan be pre-conditioned by smoothing or averaging, which can remove noiseand slightly improve the clarity of the fault discontinuities. Thisattribute volume provides better continuity of seismic horizons.

FIG. 7 shows an image 700 illustrating edge-detected volume, accordingto an implementation. For example, this volume attribute can emphasizethe degree of similarity or semblance of the seismic data and canprovide more information about lithological variation. The volumeattribute also clarifies some major discontinuities in the lower left ofthe image.

FIG. 8 shows an image 800 illustrating edge-enhanced volume using anenhanced color palette, according to an implementation. For example, theedge-enhanced volume contains much clearer information of seismicdiscontinuities when the attribute is enhanced using a customized colorpalette. Discontinuities that were previously concealed on other volumesare clearly illuminated. Conversely, information relating to seismichorizons or lithology variation is almost totally absent. An interpreterapplying inappropriate color rendering methodology with standard colorsettings may not be able to see these details and would therefore beunable to effectively interpret the data.

FIG. 9 shows an image 900 illustrating initial 3D blended volumes,according to an implementation. For example, the image 900 is a resultof blending the pre-conditioned seismic volume of FIG. 6, theedge-detected of FIG. 7, and edge-enhanced volume of FIG. 8. When theblending of attribute volumes is unclear, it may be due to incorrectmethodology, poor color representation, or incorrect opacity settings.In this example, the color representation applied to the edge-enhancedattribute is the default, which fails to reveal the hiddendiscontinuities in the volume.

FIG. 10 shows an image 1000 illustrating optimally 3D blended andilluminated seismic cubes, according to an implementation. For example,the image 1000 illustrates the effects of blending the pre-conditioned,edge-detected, and edge-enhanced cubes. When the three component volumesare blended together, e.g., using optimal transparency settings andcolor palettes, the discontinuities are clearly illuminated. Forexample, the blended display shows lithological variation from theedge-detected attribute, continuous seismic horizon detail from thepre-conditioned seismic attribute, and exceptional detail of structuraldiscontinuities from the edge-enhanced attribute. Blending for the image1000 uses, for example, illumination opacities for each of the threevolumes as shown in Table 1:

TABLE 1 Illumination Opacities Attribute Volume Illumination Opacity %Pre-conditioned 70% Edge-detected 40% Edge-enhanced 30%The optimally illuminated result shown in FIG. 10 can then be used toguide an iterative process of determining the most effective settingsfor the automated fault extraction.

For an automatic derivation of discontinuities, it is essential to haveachieved effective illumination of discontinuities through blending andcolor manipulation of multiple seismic attributes prior to commencingthe iterative automated derivation of discontinuities. Without clearillumination, it is impractical to determine optimal extractionparameters because the interpreter cannot see the faults that he aims toextract. The following examples demonstrate how an interpreter caniteratively adjust the extraction parameters to achieve meaningfulresults.

FIG. 11 shows an image 1100 illustrating automatically extracteddiscontinuities, according to an implementation. The extraction samplingthreshold (e.g., a minimum signal level from which to create extractionpoints) in this case is set to a top 10%, which only extracts thehighest data values. In addition, using the highest connectivityconstraint values—an extracted discontinuity must have voxelconnectivity on 1, 2 or 3 faces to be included in the results—means thatonly high-confidence faults are extracted. As a result, for the image1100, the automatic extraction parameters are too constrained, causingthe discontinuities in the central area to remain un-extracted.

FIG. 12 shows an image 1200 illustrating automatically extracteddiscontinuities, according to an implementation. In this example, thetop 50% of values are used. By relaxing the extraction samplingthreshold to 50%, but retaining a high connectivity constraint, forexample, more discontinuities can be extracted in the central area. Inthis display, other extracted items that are not meaningful are filteredout. The iterative process can continue until optimal results areachieved.

The decision to terminate iterations can be made, e.g., by aninterpreter based on human experience. Rather than using a particularquantitative value or threshold, a visual comparison of extracted faultswith the blended image of seismic attributes can be made until the matchappears to be satisfactory, as viewed by the interpreter. Extractionparameters can be refined on a sub-volume until the desired results areobtained.

FIG. 13 shows an illustrative workflow 1300 for refining extractionresults, according to an implementation. For example, the workflow 1300illustrates how images 600, 700, and 800 for pre-conditioned,edge-detected, and edge-enhanced volumes, respectively, are blended tocreate the image 1000. Further, an iterative process 1302 can be used tocompare the extracted results with the image 1000 prior to conversion(1304) of extracted results to fault interpretation objects.

For example, the workflow 1300 can be used in a technique that includesthe following steps. Seismic amplitude volumes are pre-conditionedthrough filtering and/or smoothing. The original seismic amplitude cubeis used to create an optimal edge-detected attribute and optimaledge-enhanced cube to enhance lithology variation and discontinuities.The discontinuities of the edge-enhanced attribute are further enhancedby applying a custom color palette. The pre-conditioned, edge-detected,and edge-enhanced cubes 600,700, 800 are blended to illuminatediscontinuities, e.g., by varying opacity in each volume. Automatedfault extraction parameters, e.g., an extraction sampling threshold andconnectivity constraints, are iteratively applied to a croppedilluminated cube to derive optimal discontinuities. Optimal extractionparameters are applied to the full edge-enhanced cube. Stereonet andhistogram tools can be used to isolate automatically extracted surfaces.A geophysical interpreter can manually refine and interpret keyextracted surfaces. Final extracted surfaces are converted toconventional faults for detailed interpretation.

FIG. 14 illustrates a table 1400 of example color spectrums 1402 usedfor techniques 1404, according to an implementation. For example, toproduce a result through interactions as described above, opacitypercentages 1406 of 70%, 40%, and 30% can be used for thepre-conditioned attribute 1408, the edge-detected attribute 1410, andthe edge-enhanced attribute 1412, respectively, in an iterative processfor automated extraction of discontinuities. As shown, color spectrum1402 a is very specific for the edge-enhanced attribute 1412, as theextreme right end of the spectrum is white, as opposed to the majoritybeing black.

In some implementations, the workflow described above can beaccomplished with the following hardware configuration. In someimplementations, a minimum hardware configuration for running PETRELgeophysical workflows can include a dual monitor standard desktopcomputer running 64 bit MICROSOFT WINDOWS 7, 64 GB memory and dualquad-core processors. In some implementations, a preferred graphicsdesktop can be a NVIDIA QUADRO K5200 or K6000. In some implementations,primary storage can be fast rotational speed HDD (10K, 15K) or 300 GBSSD. This configuration can support processing of seismic cubes, forexample, of at least 1.3 GB in size. Other hardware configurationsconsistent with this disclosure can be used and are considered to bewithin the scope of this disclosure.

The quality and usefulness of iterative fault extraction results can bedirectly proportional to a degree of illumination. For example, if theillumination methodology is bypassed or inadequately applied, thensubsequent extraction results can also be sub-optimal. Therefore, anexperienced interpreter can use his expertise to know when theillumination of discontinuities is optimal. For practical purposes, itis also necessary to determine the best fault extraction parameters on arepresentative sub-volume (e.g., a portion of the seismic cube), due tothe heavy CPU requirements of the process. Meaningful parameters can bedetermined by an experienced geophysical interpreter, in that theextracted discontinuities must represent realistic geologicaldiscontinuities as represented in the illuminated volume. Whenmeaningful parameters have been achieved, the parameters can then beapplied to the entire data area (e.g., the entire seismic cube).

Algorithms, tools, and calculations used in the techniques describedherein can be industry standard and are available using commercialsoftware. For example smoothing tools can use a smoothing attribute(e.g., generated by Schlumberger Petrel commercial software) andadditional attributes (e.g., Detect II and Skeleton, generated by Aramcoin-house algorithms) which are forms of chaos and edge-enhancementattributes respectively. All three attributes can be generated, forexample, with commercial algorithms that create similar output. Thetools used for automatic fault extraction can be, for example, fromcommercially available Schlumberger Petrel software. The overall toolused to illustrate the inventive concept can be, for example,Schlumberger Petrel software, which is a main tool used in oilexploration.

Seismic attribute generating algorithms can be used to condition theoriginal seismic data to illuminate the subsurface image. Theseattributes can be calculated with commercially available software asmentioned above. Examples include structural smooth, edge-detection,coherency, chaos, or edge-enhanced attributes. Tools used to blend theparameters, color palettes, and fault extraction parameters can includetools from Schlumberger Petrel software, although other commercialsoftware can also be used for this purpose.

Example sources and descriptions for the techniques described herein arelisted in Table 2. For example, the Table 2 lists expanded details foreach of the workflow steps described above, including data inputs andoutputs of each step:

TABLE 2 Inputs/Outputs of Workflow Description Data Input Originalseismic cube SEGY format from seismic acquisition company GeneratedOutput Smoothed cube SEGY attribute generated from input cube Chaos cubeSEGY attribute generated from input cube Edge-enhanced cube SEGYattribute generated from input cube Blended Output Graphically blendedCombined SEGY of generated cube using specific output cubes withspecific color color palettes and palettes and transparency totransparency illuminate faults parameters Automatically Extracted FaultsCommercial Petrel Iteratively generate fault software outputsdiscontinuities from original ‘fault patches’ in internal seismic cubeand view on format using a blended seismic until the representativesub-volume output matches the geometry for faster performance of the‘illuminated’ discontinuities of the blended cube. The iteration stopswhen the interpreter is satisfied that the match represents the imagedfault distribution. Final Automatic Fault Extraction Run with optimalparameters Performed on entire input seismic determined from iterationcube (this may take many hours depending on seismic cube size) RefineExtracted Faults Manually refine Merge, smooth fault output untilsatisfied automatically the main elements are representative extractedfaults of the imaged blended seismic Convert Extracted Faults Convertautomatically Interpreter uses the extracted extracted faults totraditional results to continue interpretation fault interpretation forin a traditional way further interpretation & analysis

FIG. 15 illustrates a method 1500 for blending multiple 3D seismicvolume attributes and applying techniques to make discontinuities standout, according to an implementation. For clarity of presentation, thedescription that follows generally describes method 1500 in the contextof FIGS. 1-14. Method 1500 may be performed by any suitable system,environment, software, and/or hardware, or a combination of systems,environments, software, and/or hardware as appropriate (e.g., thecomputer system described in FIG. 16 below). In some implementations,various steps of method 1500 can be run in parallel, in combination, inloops, or in any order.

At 1502, plural attributes are selected for annotating images generatedfrom, and for interpretation of, seismic amplitude volume. For example,the attributes that are selected can include pre-conditioned seismicattributes, various edge-detection attributes, and edge-enhancedattributes, and/or other combinations of attributes that have been shownto produce optimal results. From 1502, method 1500 proceeds to 1504.

At 1504, plural images are generated from a set of layers of a seismiccube, each image generated using a different attribute of the pluralattributes (and using optimal parameters). For example, imagesassociated with the attributes, including images 600, 700, and 800, canbe generated, as described above with reference to FIGS. 6, 7, and 8,respectively. From 1504, method 1500 proceeds to 1506.

At 1506, the plural images are blended using customized palettes andinitial parameters for each of the plural images. The blending creates ablended image that illuminates discontinuities in the set of layers.Referring to FIG. 10, for example, the image 1000 illustrates initial 3Dblended volumes from the pre-conditioned seismic volume of FIG. 6, theedge-detected of FIG. 7, and edge-enhanced volume of FIG. 8. From 1506,method 1500 proceeds to 1508. In some implementations, blending theplural images can include using specific customized color palettes(e.g., color spectrums 1402).

At 1508, optimal parameters are iteratively determined for automaticderivation of fault discontinuities on an interpreter-selectededge-enhanced sub-volume. The iterations are controlled and terminatedbased on inputs from an interpreter and/or other sources. As describedabove with reference to FIG. 10, for example, the image 1000 illustratesoptimally 3D blended and illuminated seismic cubes, in which blendingoccurs for the pre-conditioned, edge-detected, and edge-enhanced cubes.When the three component volumes are blended together, e.g., usingoptimal transparency settings and color palettes, the discontinuitiesare clearly illuminated. The optimally illuminated result shown in FIG.10 can then be used to guide an iterative process of determining themost effective settings for the automated fault extraction. Referring toFIG. 11, the image 1100 illustrates automatically extracteddiscontinuities. In this example, the extraction sampling threshold isset to a top 10%, which only extracts the highest data values. Referringto FIG. 12, the image 1200 illustrates automatically extracteddiscontinuities, e.g., in which the top 50% of values are used. From1508, method 1500 proceeds to 1510.

At 1510, the optimal extraction parameters are applied to an entireedge-enhanced volume. For example, the parameters determined from theiterative process can be used on the entire edge-enhanced volume of theoriginal seismic cube. From 1510, method 1500 proceeds to 1512.

At 1512, important extracted fault discontinuities are isolated usingcommercial filtering tools. For example, stereonet, histogram, and/orother tools can be used alone or in combination to isolate automaticallyextracted surfaces. From 1514, method 1500 proceeds to 1514.

At 1514, extracted fault patches are refined based on received manualinterpretation from the interpreter. For example, using commercialsoftware, a geophysical interpreter can manually refine and interpretkey extracted surfaces. From 1514, method 1500 proceeds to 1516.

At 1516, patch results are converted to traditional fault objects forfurther interpretation and refinement. For example, important seismicdiscontinuities that may otherwise remain hidden using traditionalmethodologies can be effectively illuminated, and turnaround time foriterative automated fault extraction and interpretation can bedecreased. From 1516, method 1500 stops.

FIG. 16 is a block diagram illustrating an exemplary distributedcomputer system (EDCS) 1600 used for providing parameters for successfulautomated fault patch extraction, according to an implementation. Insome implementations, the EDCS 1600 includes a computer 1602, andnetwork 1630.

The illustrated computer 1602 is intended to encompass a computingdevice such as a server, desktop computer, laptop/notebook computer,wireless data port, smart phone, personal data assistant (PDA), tabletcomputing device, one or more processors within these devices, or anyother suitable processing device, including both physical and/or virtualinstances of the computing device. The computer 1602 may comprise acomputer that includes an input device, such as a keypad, keyboard,touch screen, or other device (not illustrated) that can accept userinformation, and an output device (not illustrated) that conveysinformation associated with the operation of the computer 1602,including digital data, visual and/or audio information, or a userinterface.

The computer 1602 can serve as a client and/or a server. The illustratedcomputer 1602 is communicably coupled with a network 1630. In someimplementations, one or more components of the computer 1602 may beconfigured to operate within a parallel-processing and/orcloud-computing-based environment. Implementations of the computer 1602can also communicate using message passing interface (MPI) or otherinterface over network 1630.

At a high level, the computer 1602 is an electronic computing deviceoperable to receive, transmit, process, store, or manage data andinformation associated with modeling of reservoir formations andlithofacies distribution. According to some implementations, thecomputer 1602 may also include or be communicably coupled with asimulation server, application server, e-mail server, web server,caching server, streaming data server, business intelligence (BI)server, and/or other server.

The computer 1602 can receive requests over network 1630 from anapplication 1607 (e.g., executing on another computer 1602) andresponding to the received requests by processing the said requests inan appropriate software application 1607. In addition, requests may alsobe sent to the computer 1602 from internal users (e.g., from a commandconsole or by other appropriate access method), external orthird-parties, other automated applications, as well as any otherappropriate entities, individuals, systems, or computers.

Each of the components of the computer 1602 can communicate using asystem bus 1603. In some implementations, any and/or all the componentsof the computer 1602, both hardware and/or software, may interface witheach other and/or the interface 1604 over the system bus 1603 using anapplication programming interface (API) 1612 and/or a service layer1613. The API 1612 may include specifications for routines, datastructures, and object classes. The API 1612 may be eithercomputer-language independent or dependent and refer to a completeinterface, a single function, or even a set of APIs. The service layer1613 provides software services to the computer 1602 and/or system ofwhich the computer 1602 is a part. The functionality of the computer1602 may be accessible for all service consumers using this servicelayer. Software services, such as those provided by the service layer1613, provide reusable, defined business functionalities through adefined interface. For example, the interface may be software written inJAVA, C++, or other suitable language providing data in extensiblemarkup language (XML) format or other suitable format. While illustratedas an integrated component of the computer 1602, alternativeimplementations may illustrate the API 1612 and/or the service layer1613 as stand-alone components in relation to other components of thecomputer 1602. Moreover, any or all parts of the API 1612 and/or theservice layer 1613 may be implemented as child or sub-modules of anothersoftware module, enterprise application, or hardware module withoutdeparting from the scope of this disclosure.

The computer 1602 includes an interface 1604. Although illustrated as asingle interface 1604 in FIG. 16, two or more interfaces 1604 may beused according to particular needs, desires, or particularimplementations of the computer 1602. The interface 1604 is used by thecomputer 1602 for communicating with other systems in a distributedenvironment—including a parallel processing environment—connected to thenetwork 1630 (whether illustrated or not). Generally, the interface 1604comprises logic encoded in software and/or hardware in a suitablecombination and operable to communicate with the network 1630. Morespecifically, the interface 1604 may comprise software supporting one ormore communication protocols associated with communications over network1630.

The computer 1602 includes a processor 1605. Although illustrated as asingle processor 1605 in FIG. 16, two or more processors may be usedaccording to particular needs, desires, or particular implementations ofthe computer 1602. Generally, the processor 1605 executes instructionsand manipulates data to perform the operations of the computer 1602.Specifically, the processor 1605 executes the functionality required tomodel reservoir formations and lithofacies distribution.

The computer 1602 also includes a memory 1606 that holds data for thecomputer 1602 and/or other components of a system of which the computeris a part. Although illustrated as a single memory 1606 in FIG. 16, twoor more memories may be used according to particular needs, desires, orparticular implementations of the computer 1602. While memory 1606 isillustrated as an integral component of the computer 1602, inalternative implementations, memory 1606 can be external to the computer1602. In some implementations, memory 1606 can hold and/or reference oneor more of, as described above, a seismic cube 1614 and images 1616(e.g., images 500, 600, 700, 800, 900, 1100, and 1200).

The application 1607 is an algorithmic software engine providingfunctionality according to particular needs, desires, or particularimplementations of the computer 1602 and/or a system of which thecomputer 1602 is a part, particularly with respect to functionalityrequired to support processes for automated fault extraction andinterpretation, as described above. In some implementations, softwareapplications can include one or more of the above-described. Althoughillustrated as a single application 1607, the application 1607 may beimplemented as multiple applications 1607 on the computer 1602. Inaddition, although illustrated as integral to the computer 1602, inalternative implementations, the application 1607 can be external to andexecute apart from the computer 1602.

There may be any number of computers 1602 associated with a computersystem performing functions consistent with this disclosure. Further,the term “client,” “user,” and other appropriate terminology may be usedinterchangeably as appropriate without departing from the scope of thisdisclosure. Moreover, this disclosure contemplates that manyusers/processes may use one computer 1602, or that one user/process mayuse multiple computers 1602.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly-embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Implementations of the subject matter described inthis specification can be implemented as one or more computer programs,i.e., one or more modules of computer program instructions encoded on atangible, non-transitory computer-storage medium for execution by, or tocontrol the operation of, data processing apparatus. Alternatively or inaddition, the program instructions can be encoded on anartificially-generated propagated signal, e.g., a machine-generatedelectrical, optical, or electromagnetic signal that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. The computer-storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofone or more of them.

The term “data processing apparatus” refers to data processing hardwareand encompasses all kinds of apparatus, devices, and machines forprocessing data, including by way of example, a programmable processor,a computer, or multiple processors or computers. The apparatus can alsobe or further include special purpose logic circuitry, e.g., a centralprocessing unit (CPU), a co-processor (e.g., a graphics/visualprocessing unit (GPU/VPU)), a FPGA (field programmable gate array), oran ASIC (application-specific integrated circuit). In someimplementations, the data processing apparatus and/or special purposelogic circuitry may be hardware-based and/or software-based. Theapparatus can optionally include code that creates an executionenvironment for computer programs, e.g., code that constitutes processorfirmware, a protocol stack, a database management system, an operatingsystem, or a combination of one or more of them. The present disclosurecontemplates the use of data processing apparatuses with or withoutconventional operating systems, for example LINUX, UNIX, WINDOWS, MACOS, ANDROID, IOS or any other suitable conventional operating system.

A computer program, which may also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code, can be written in any form of programming language,including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program may, butneed not, correspond to a file in a file system. A program can be storedin a portion of a file that holds other programs or data, e.g., one ormore scripts stored in a markup language document, in a single filededicated to the program in question, or in multiple coordinated files,e.g., files that store one or more modules, sub-programs, or portions ofcode. A computer program can be deployed to be executed on one computeror on multiple computers that are located at one site or distributedacross multiple sites and interconnected by a communication network.While portions of the programs illustrated in the various figures areshown as individual modules that implement the various features andfunctionality through various objects, methods, or other processes, theprograms may instead include a number of sub-modules, third-partyservices, components, libraries, and such, as appropriate. Conversely,the features and functionality of various components can be combinedinto single components as appropriate.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., a CPU, a FPGA, or an ASIC.

Computers suitable for the execution of a computer program can be basedon general or special purpose microprocessors, both, or any other kindof CPU. Generally, a CPU will receive instructions and data from aread-only memory (ROM) or a random access memory (RAM) or both. Theessential elements of a computer are a CPU for performing or executinginstructions and one or more memory devices for storing instructions anddata. Generally, a computer will also include, or be operatively coupledto, receive data from or transfer data to, or both, one or more massstorage devices for storing data, e.g., magnetic, magneto-optical disks,or optical disks. However, a computer need not have such devices.Moreover, a computer can be embedded in another device, e.g., a mobiletelephone, a personal digital assistant (PDA), a mobile audio or videoplayer, a game console, a global positioning system (GPS) receiver, or aportable storage device, e.g., a universal serial bus (USB) flash drive,to name just a few.

Computer-readable media (transitory or non-transitory, as appropriate)suitable for storing computer program instructions and data include allforms of non-volatile memory, media and memory devices, including by wayof example semiconductor memory devices, e.g., erasable programmableread-only memory (EPROM), electrically-erasable programmable read-onlymemory (EEPROM), and flash memory devices; magnetic disks, e.g.,internal hard disks or removable disks; magneto-optical disks; andCD-ROM, DVD+/−R, DVD-RAM, and DVD-ROM disks. The memory may storevarious objects or data, including caches, classes, frameworks,applications, backup data, jobs, web pages, web page templates, databasetables, repositories storing business and/or dynamic information, andany other appropriate information including any parameters, variables,algorithms, instructions, rules, constraints, or references thereto.Additionally, the memory may include any other appropriate data, such aslogs, policies, security or access data, reporting files, as well asothers. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, implementations of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube), LCD (liquidcrystal display), LED (Light Emitting Diode), or plasma monitor, fordisplaying information to the user and a keyboard and a pointing device,e.g., a mouse, trackball, or trackpad by which the user can provideinput to the computer. Input may also be provided to the computer usinga touchscreen, such as a tablet computer surface with pressuresensitivity, a multi-touch screen using capacitive or electric sensing,or other type of touchscreen. Other kinds of devices can be used toprovide for interaction with a user as well; for example, feedbackprovided to the user can be any form of sensory feedback, e.g., visualfeedback, auditory feedback, or tactile feedback; and input from theuser can be received in any form, including acoustic, speech, or tactileinput. In addition, a computer can interact with a user by sendingdocuments to and receiving documents from a device that is used by theuser; for example, by sending web pages to a web browser on a user'sclient device in response to requests received from the web browser.

The term “graphical user interface,” or GUI, may be used in the singularor the plural to describe one or more graphical user interfaces and eachof the displays of a particular graphical user interface. Therefore, aGUI may represent any graphical user interface, including but notlimited to, a web browser, a touch screen, or a command line interface(CLI) that processes information and efficiently presents theinformation results to the user. In general, a GUI may include aplurality of UI elements, some or all associated with a web browser,such as interactive fields, pull-down lists, and buttons operable by thebusiness suite user. These and other UI elements may be related to orrepresent the functions of the web browser.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back-endcomponent, e.g., as a data server, or that includes a middlewarecomponent, e.g., an application server, or that includes a front-endcomponent, e.g., a client computer having a graphical user interface ora Web browser through which a user can interact with an implementationof the subject matter described in this specification, or anycombination of one or more such back-end, middleware, or front-endcomponents. The components of the system can be interconnected by anyform or medium of wireline and/or wireless digital data communication,e.g., a communication network. Examples of communication networksinclude a local area network (LAN), a radio access network (RAN), ametropolitan area network (MAN), a wide area network (WAN), WorldwideInteroperability for Microwave Access (WIMAX), a wireless local areanetwork (WLAN) using, for example, 802.11 a/b/g/n and/or 802.20, all ora portion of the Internet, and/or any other communication system orsystems at one or more locations. The network may communicate with, forexample, Internet Protocol (IP) packets, Frame Relay frames,Asynchronous Transfer Mode (ATM) cells, voice, video, data, and/or othersuitable information between network addresses.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

In some implementations, any or all of the components of the computingsystem, both hardware and/or software, may interface with each otherand/or the interface using an application programming interface (API)and/or a service layer. The API may include specifications for routines,data structures, and object classes. The API may be either computerlanguage independent or dependent and refer to a complete interface, asingle function, or even a set of APIs. The service layer providessoftware services to the computing system. The functionality of thevarious components of the computing system may be accessible for allservice consumers via this service layer. Software services providereusable, defined business functionalities through a defined interface.For example, the interface may be software written in JAVA, C++, orother suitable language providing data in extensible markup language(XML) format or other suitable format. The API and/or service layer maybe an integral and/or a stand-alone component in relation to othercomponents of the computing system. Moreover, any or all parts of theservice layer may be implemented as child or sub-modules of anothersoftware module, enterprise application, or hardware module withoutdeparting from the scope of this disclosure.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or on the scope of what may be claimed, but rather asdescriptions of features that may be specific to particularimplementations of particular inventions. Certain features that aredescribed in this specification in the context of separateimplementations can also be implemented in combination in a singleimplementation. Conversely, various features that are described in thecontext of a single implementation can also be implemented in multipleimplementations separately or in any suitable sub-combination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation and/or integration ofvarious system modules and components in the implementations describedabove should not be understood as requiring such separation and/orintegration in all implementations, and it should be understood that thedescribed program components and systems can generally be integratedtogether in a single software product or packaged into multiple softwareproducts.

Particular implementations of the subject matter have been described.Other implementations, alterations, and permutations of the describedimplementations are within the scope of the following claims as will beapparent to those skilled in the art. For example, the actions recitedin the claims can be performed in a different order and still achievedesirable results.

Accordingly, the above description of example implementations does notdefine or constrain this disclosure. Other changes, substitutions, andalterations are also possible without departing from the spirit andscope of this disclosure.

What is claimed is:
 1. A non-transitory, computer-readable medium storing computer-readable instructions executable by a computer and configured to: selecting plural attributes for annotating images generated from, and for interpretation of, seismic amplitude volume; generating plural images from a set of layers of a seismic cube, each image generated using a different attribute of the plural attributes; blending the plural images, using customized palettes and initial parameters for each of the plural images, the blending creating a blended image that illuminates discontinuities in the set of layers; iteratively determining optimal parameters for automatic derivation of fault discontinuities on an interpreter-selected edge-enhanced sub-volume, wherein iterations are controlled and terminated based on inputs from an interpreter; applying the optimal extraction parameters to an entire edge-enhanced volume; isolating important extracted fault discontinuities using commercial filtering tools; refining extracted fault patches based on received manual interpretation from the interpreter; and converting patch results to traditional fault objects for further interpretation and refinement.
 2. The non-transitory, computer-readable medium of claim 1, further configured to receive the seismic cube.
 3. The non-transitory, computer-readable medium of claim 1, wherein the plural attributes include pre-conditioned seismic attributes, edge-detection attributes, and edge-enhanced attributes, and wherein blending the plural images includes blending images generated from the pre-conditioned seismic attributes, the edge-detection attributes, and the edge-enhanced attributes.
 4. The non-transitory, computer-readable medium of claim 3, wherein the optimal parameters for the pre-conditioned seismic attributes, the edge-detection attributes, and the edge-enhanced attributes include opacities of 70%, 40%, and 30%, respectively.
 5. The non-transitory, computer-readable medium of claim 1, wherein the initial parameters include a 10% extraction sampling threshold.
 6. The non-transitory, computer-readable medium of claim 3, wherein blending the plural images further includes using specific customized color palettes. 